PD-L1 regulates cell proliferation and apoptosis in acute myeloid leukemia by activating PI3K-AKT signaling pathway

As immune checkpoint inhibitors (ICIs) continue to advance, more evidence has emerged that anti-PD-1/PD-L1 immunotherapy is an effective treatment against cancers. Known as the programmed death ligand-1 (PD-L1), this co-inhibitory ligand contributes to T cell exhaustion by interacting with programmed death-1 (PD-1) receptor. However, cancer-intrinsic signaling pathways of the PD-L1 molecule are not well elucidated. Therefore, the present study aimed to evaluate the regulatory network of PD-L1 and lay the basis of successful use of anti-PD-L1 immunotherapy in acute myeloid leukemia (AML). Data for AML patients were extracted from TCGA and GTEx databases. The downstream signaling pathways of PD-L1 were identified via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The key PD-L1 related genes were selected by weighted gene co-expression network analysis (WGCNA), MCC algorithm and Molecular Complex Detection (MCODE). The CCK-8 assay was used to assess cell proliferation. Flow cytometry was used to determine cell apoptosis and cell cycle. Western blotting was used to identify the expression of the PI3K-AKT signaling pathway. PD-L1 was shown to be elevated in AML patients when compared with the control group, and high PD-L1 expression was associated with poor overall survival rate. The ECM-receptor interaction, as well as the PI3K-AKT signaling pathway, were important PD-L1 downstream pathways. All three analyses found eight genes (ITGA2B, ITGB3, COL6A5, COL6A6, PF4, NMU, AGTR1, F2RL3) to be significantly associated with PD-L1. Knockdown of PD-L1 inhibited AML cell proliferation, induced cell apoptosis and G2/M cell cycle arrest. Importantly, PD-L1 knockdown reduced the expression of PI3K and p-AKT, but PD-L1 overexpression increased their expression. The current study elucidates the main regulatory network and downstream targets of PD-L1 in AML, assisting in the understanding of the underlying mechanism of anti-PD-1/PD-L1 immunotherapy and paving the way for clinical application of ICIs in AML.

Differentially expressed genes and pathway analysis. Patients were divided into 2 groups according to PD-L1 expression level in AML. Limma R package was utilized for differential analysis of the gene expression profiles, and the method of false discovery rate (FDR) was applied to adjust the p value, with [log2 fold change] > 2 and FDR < 0.05 set as the filtering threshold. These differentially expressed genes (DEGs) derived from differential analysis were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis using KOBAS online website and clusterProfiler R software packages. KOBAS (KEGG Orthology Based Annotation System) is an extensive web-version database (http:// kobas. cbi. pku. edu. cn/ kobas3/) mapped to known gene/protein functions for annotation and feature set enrichment 22 . The clusterProfiler R package for comparing biological themes between gene clusters was used to show the functional diversity of three different GO terms 23 , including biological processes (BP), cell components (CC) and molecular functions (MF).

Identification of PD-L1-associated genes.
To identify the gene set that is closely related to PD-L1 in AML, weighted gene co-expression network analysis (WGCNA) was performed. The information of most variable genes was used to identify the DEGs and conduct association analysis with PD-L1 expression for WGCNA 24 . Intramodular connectivity is defined as the degree of association between a given gene and other genes in the modules to determine the connection between genes. Module membership is characterized as the correlation between gene expression profiles and modules. The adjacency matrix was constructed by selecting the optimal soft threshold complying with intramodular connectivity and module membership. To reduce the influence of noise and spurious associations, the adjacency matrix was converted to a topological overlap matrix (TOM). To classify the TOM into the gene modules, dynamic tree cut was performed and the correlation between the module and PD-L1 expression was visualized with a heatmap.
Subsequently, plug-in cytoHubba from Cytoscape was used to assign values to each gene with the topological network algorithm MCC, and the hub genes were found 25 . In addition, the Molecular Complex Detection (MCODE) plug-in of Cytoscape software was employed to explore important modules or sub-networks in the PPI network. A Venn diagram was used to find the important PD-L1 related genes in WGCNA, MCC and MCODE.
Statistical analysis. For bioinformatic analysis, the p value was calculated by the Wilcoxon or Kruskal-Wallis test. Survival analysis was performed using Kaplan-Meier (KM) curve, and the differences between the survival curves were determined via the log-rank test. The R-value of the correlation analysis was calculated by Pearson's analysis. Experiment data were presented as mean ± SD. All experiments were performed in triplicates and repeated three times. Statistical analysis was performed using GraphPad Prism 8.3.4. The t-test or one-way analysis were performed to compare the significance of difference between two or more groups. A p value < 0.05 was considered statistically significant.

PD-L1 expression was associated with clinicopathological parameters.
To determine the expression of PD-L1 in AML, bioinformatic analysis was performed using 70 normal and 173 AML patients' data from TCGA database. The results revealed that the PD-L1 expression level was significantly up-regulated in AML (Fig. 1A). In addition, single-cell sequencing data of CD34+ hematopoietic stem and progenitor cells from 2 AML patients and 2 healthy individuals showed that the expression of PD-L1 was significantly higher in AML than in healthy individuals ( Figure S1). High expression of PD-L1 was significantly associated with poor prognosis (Fig. 1B). Since tumor mutational burden (TMB) is indicative of immunotherapy response and is associated with PD-L1 expression 26 , we analyzed its association with patient overall survival (OS). As shown in Figure S2, higher TMB tended to be associated with worse patient OS, but not significantly. As shown in Fig. 1C, the association between PD-L1 expression and clinicopathological parameters was further assessed. The results showed the expression of PD-L1 in elderly AML patients (age > 65) was significantly higher than that in younger patients (Fig. 1C). The PD-L1 expression was also significantly higher in patients with poor cytogenetics background than in favorable cytogenetics patients. Moreover, PD-L1 expression exhibited significant differences for FAB morphology, with a particularly higher expression level in M6 and M7 (Fig. 1C).
KEGG and GO enrichment analysis of PD-L1 related DEGs. In this study, 294 differentially expressed genes (DEGs) were obtained from differential analysis based on the expression of PD-L1 in tumor as displayed in the volcano plot ( Fig. 2A). In order to find the specific signaling pathways and functions of these DEGs, the KOBAS online website and clusterProfiler R software packages were utilized to perform enrichment analysis. The top 15 signaling pathways were visualized using lollipop plot (Fig. 2B). These DEGs were enriched in various signaling pathways. Among signaling pathways, the PI3K-AKT and ECM-receptor interaction pathway were the most significant pathways. Moreover, GO analysis showed that these DEGs were mainly involved in pattern specification processes (biological processes), collagen-containing extracellular matrix (cell components) and sulfur compound activity (molecular functions) in AML (Fig. 2C).
Identification of genes associated with PD-L1 expression. The 294 DEGs obtained from the differential analysis were included for weighted gene co-expression network analysis (WGCNA). The optimal soft www.nature.com/scientificreports/ threshold was set to construct the adjacency matrix and the topological overlap matrix (TOM) (Fig. 3A). Then the genes in TOM were divided into gene sets by Dynamic Tree Cut, and four modules were generated (Fig. 3B). Among the four modules, two modules in red and yellow color showed the strongest significant association with PD-L1 expression and were chosen for further analysis (Fig. 3C).
To further find the important downstream targets of PD-L1, the top 20 key DEGs were visualized by MCC in Cytoscape (Fig. 4A). Moreover, a key sub-network composed of 21 genes was constructed by MCODE (Fig. 4B). By overlapping the results of MCC, MOCDE, and WGCNA in a Venn diagram, eight genes were demonstrated to be important PD-L1 related genes (Fig. 4C). Intriguingly, four out of the eight genes were simultaneously enriched in PI3K-AKT signaling pathway and ECM-receptor interaction, which is consistent with previous enrichment analysis (Fig. 2C). Therefore, we extracted the expression data of PD-L1 and all the enriched genes in these two pathways for correlation analysis. From the result, we observed that most of the enriched genes in the abovementioned pathways were not only strongly correlated with PD-L1, but also had significant correlation among themselves (Fig. 4D), suggesting that these genes may collaborate to mediate the function of PD-L1 in AML. Bioinformatics results illustrated that the key PD-L1 related genes were involved in the ECM-receptor interaction and PI3K-AKT signaling pathways, suggesting that PD-L1 may functionally promote AML leukemogenesis, such as proliferation, apoptosis and cell cycle (Fig. 4E).
To study the effect of PD-L1 expression on biological activities of leukemic cells, the cell proliferation of siRNA PD-L1-transfected KG-1a cells, as well as PD-L1-overexpressed EoL-1 cells, was evaluated. It was shown that the proliferation rate of KG-1a with siRNA PD-L1 groups was significantly lower than that of KG-1a transfected siNC control group (Fig. 5D). Upon PD-L1 overexpression, the proliferation rate of EoL-1 with PD-L1 overexpressing group was significantly higher than that of the EoL-1 with vector control group (Fig. 5D). This result indicated that overexpressed PD-L1 enhanced cell proliferation of AML cell lines.
In order to explore the effects of PD-L1 knockdown on cell apoptosis and cell cycle, apoptotic rates and cell cycle distribution were performed using flow cytometry. As shown in Fig. 5E, the percentages of apoptotic cells were 11.46% and 12.08% in siPD-L1#1 and siPD-L1#2 group, respectively, compared with 7.69% in siNC group (P < 0.01), suggesting that the downregulation of PD-L1 expression could promote cell apoptosis in AML leukemic cell line. In addition, the number of KG-1a cells with PD-L1 silencing (siPD-L1#2 group) in the G2/M phase was significantly increased when compared with NC group (siNC) (Fig. 5F)  www.nature.com/scientificreports/ p-AKT after knockdown or overexpression of PD-L1 in KG-1a and EoL-1 cells were investigated by Western blotting. After PD-L1 knockdown, the expression of PI3K and p-AKT were decreased in KG-1a cells (Fig. 6A). Conversely, PI3K, AKT and p-AKT expression were increased in KG-1a and EoL-1 cells after overexpressing PD-L1 (Fig. 6A). Further, CCK-8 assay found that pharmacological inhibition of AKT by MK-2206 completely abolished PD-L1-promoted cell proliferation in KG-1a overexpression PD-L1 compared to vector control group (Fig. 6B). This is consistent with our results from bioinformatics results that PD-L1 facilitates tumor progression of AML through the PI3K-AKT signaling pathway.

Discussion
The present study primarily focused on the association of PD-L1 and biological activities in AML cells. The five-year overall survival (OS) of AML patients has considerably improved over the last decades due to a better understanding of targeted therapies and immunotherapies 27,28 . PD-1/PD-L1 inhibitors are potentially useful in combination with hypomethylating agents at consolidation or maintenance stage, or after allogenic hematopoietic stem cell transplantation (allo-HSCT). However, the successful use of checkpoint inhibitors in AML still awaits further investigation and clinical studies 27,28 . PD-L1 overexpression is usually found in AML during therapy or at relapse and positivity of PD-L1 is often associated with adverse clinical outcome 29 . Expression of PD-L1 in AML might be stimulated by cytokines like IFN-γ or TP53 mutation 30,31 . Nonetheless, the downstream pathways mediating PD-L1 functions are not well elucidated. In the current study, using a series of bioinformatics methods, www.nature.com/scientificreports/ we first explored the expression level of PD-L1 and its association with survival and clinicopathological parameters using publicly available data. Our results revealed that PD-L1 was significantly upregulated in AML tumor tissues compared with normal ones (Fig. 1A) and high expression of PD-L1 was significantly associated with worse patient survival (Fig. 1B). High expression of PD-L1 was also significantly associated with older age and poor cytogenetics (Fig. 1C). Cytogenetics is important for monitoring disease dynamics, response assessment, and characterization of clonal evolution in AML and can be used to stratify prognostic risk of AML patients 32 .
To explore the detailed regulatory mechanism of PD-L1 in AML patients, we divided the AML patients into 2 groups according to the PD-L1 expression level for differential analysis. 294 DEGs were found ( Fig. 2A) and subsequently subjected to enrichment analysis, including KEGG pathway enrichment and GO functional annotation analysis. These DEGs were significantly enriched in two pathways: the P13K-AKT signaling pathway and the ECM-receptor interaction. By querying these two pathways, it was determined that the ECM-receptor pathway is upstream of the PI3K-AKT pathway and acts on the PI3K-AKT pathway through a series of genes 33 . It has been reported that PD-L1 expression sustains stemness factors OCT-4A and Nanog, via a PI3K/AKT-dependent pathway, and promotes expression of the stemness controlling factor BMI1, independent of PI3K/AKT in breast cancer cells 20 . In lung cancer, PD-L1 promotes cell proliferation, migration and invasion by activating PD-L1/ AKT/β-catenin/WIP signaling pathway 19 . Research evidence also suggested that PD-L1 directly interacts with HMGA1 and activates HMGA1-dependent pathways, including the PI3K/AKT and MEK/ERK pathways in colorectal cancer 34 . Thus, it can be concluded that the action of PD-L1 is closely related to the PI3K/AKT pathway.
To further screen out genes more closely linked to PD-L1 expression, WGCNA analysis on the expression matrix composed of DEGs based on PD-L1 expression was performed. Based on the selected conditions, two modules of 117 genes in total were obtained for further analysis (Fig. 3B,C). In addition, 20 key genes of PD-L1 were found by MCC topology algorithm in Cytoscape software (Fig. 4A). At the same time, a key sub-network of 21 genes was constructed by MCODE (Fig. 4B). By overlapping the discovered genes using three methods in a Venn diagram (Fig. 4C), eight genes were predicted to be the key PD-L1 related genes, namely ITGA2B, ITGB3, COL6A5, COL6A6, PF4, NMU, AGTR1 and F2RL3. Moreover, these genes were strongly correlated with PD-L1  www.nature.com/scientificreports/ (Fig. 4D). It has been reported in the literature that ITGA2 plays a critical role in cancer cell progression and the regulation of PD-L1 by activating the STAT3 pathway 35 . PD-L1 (CD274) expression is positively correlated with ITGB3 in many cancers 36 . For COL6A5 and COL6A6, previous research evidence suggests that COL6A5 is closely associated with atopic dermatitis 37 . It is also worth noting that the results of our analysis and previous studies have suggested that COL6A6 can function through the PI3K-AKT pathway 38 . PF4 (Platelet factor 4) is a growth regulator of hematopoietic stem/progenitor cells (HSPCs) 39 . It has been reported that the protein level of PF4 is a good indicator of the recovery of blood count in complete remission of acute myeloid leukemia 40 . The complex formed by the binding of PF-4 and heparin is an important etiology of Heparin-induced thrombocytopenia (HIT) 41 . ATGR1 (The angiotensin II type I receptor) has been well-reported to be overexpressed in cancer and its inhibition can attenuate tumorigenicity 42,43 . The Ang II-AGTR1 axis induced an inhibitory immune TME by upregulating PD-L1 in non-small-cell lung cancer 44 . It is also a potential therapeutic target of breast cancer 45 . However, its role in AML has not been reported. F2RL3 (F2R Like Thrombin/Trypsin Receptor 3) has been reported to be associated with smoking and F2RL3 methylation is a very strong predictor of mortality 46,47 . Its role in AML is also not clarified. Together, our results indicate that PD-L1 is strongly related to genes that are closely associated with cancer progression and prognosis. Recent data have mentioned the distinct tumor-intrinsic role of PD-L1 in promoting cancer initiation, metastasis, development and resistance to therapy 10 . Our study demonstrated that downregulated PD-L1 expression in AML cell line KG-1a significantly inhibited cell proliferation, along with induction of G2/M phase arrest, and apoptosis induction (Fig. 5). These results were consistent with the findings in other human cancers. In human breast cancer, it has been reported that the PD-L1 expression level was significantly associated with a high ratio of proliferating cancer cells 48 and that the overexpression of PD-L1 promotes tumor cell growth 19 . Furthermore, knockdown of PD-L1 expression in gastric cancer cells could significantly suppress cell proliferation, migration, invasion and promote apoptosis 16 . In the present study, knockdown of PD-L1 in KG-1a cells lead to downregulated PI3K, AKT and p-AKT expression, whereas PD-L1 overexpression in EoL-1 cells had the opposite effects (Fig. 6A). Furthermore, AKT inhibitor significantly inhibited the proliferation of PD-L1-overexpressing KG-1a cells (Fig. 6B). This result indicated that PD-L1 may regulate the biological functions of AML cell line via PI3K/ AKT signaling pathway.

Conclusions
We have observed close association between PD-L1 expression and AML in the TCGA and GTEx gene expression dataset, and experimental data confirmed this association and demonstrated the critical role of PD-L1 in cell proliferation, cell cycle and apoptosis. Moreover, both bioinformatic analyses and experimental data suggested that the underlying mechanism of PD-L1 in AML is mediated through PI3K/AKT activation. This is the first report revealing the key downstream targets and signaling pathways of PD-L1 in AML, which might help in the realization of anti-PD-1/PD-L1 immunotherapy in AML.

Data availability
AML expression matrix data were obtained by the sanger box tool. Click TCGA RNA-seq Easy Converter to acquire and convert Count to TPM format. PD-L1 expression was obtained from the AML RNA-Seq data in the TCGA database and the normal tissue RNA-Seq data in the GTEx database obtained from the official website of UCSC Xena (https:// xenab rowser. net/ heatm ap/). RNA-seq expression level was obtained by searching for CD274(PD-L1) after selecting the TCGA target GTEx under the VISUALIZATION subheading of the link provided.